An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

Bhupesh Kumar Dewangan, Venkatadri M., Amit Agarwal, Ashutosh Pasricha, Tanupriya Choudhury
Copyright: © 2021 |Pages: 18
DOI: 10.4018/IJGHPC.2021010103
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Abstract

In cloud computing, applications, administrations, and assets have a place with various associations with various goals. Elements in the cloud are self-sufficient and self-adjusting. In such a collaborative environment, the scheduling decision on available resources is a challenge given the decentralized nature of the environment. Fault tolerance is an utmost challenge in the task scheduling of available resources. In this paper, self-healing fault tolerance techniques have been introducing to detect the faulty resources and measured the best resource value through CPU, RAM, and bandwidth utilization of each resource. Through the self-healing method, less than threshold values have been considering as a faulty resource and separate from the resource pool. The workloads submitted by the user have been assigned to the available best resource. The proposed method has been simulated in cloudsim and compared the multi-objective performance metrics with existing methods, and it is observed that the proposed method performs utmost.
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Background

There are different shortcomings, which can happen in cloud/distributed computing. Based on adaptation to internal failure arrangements different adaptation to internal failure strategies can be utilized that can be either resource monitoring, management or scheduling level. Fault tolerance techniques can be further divide into two categories, Reactive and Proactive. Reactive can be further categorize into checkpoint restart, job migration and replication as well as proactive also divided into preemptive migration, system rejuvenation, and self-healing.

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